Neural variance-aware dueling bandit algorithms achieve sublinear regret with network width m = Omega~(T^6), an improvement over the previous Omega~(T^14), under both UCB and Thompson sampling.
Federated neural bandits
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Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration
Neural variance-aware dueling bandit algorithms achieve sublinear regret with network width m = Omega~(T^6), an improvement over the previous Omega~(T^14), under both UCB and Thompson sampling.